In the information age many information systems (e.g., credit card systems, driver's license management systems) have been developed which rely on assertions of identity by people being offered services through the systems. Moreover, as modern societies have developed, the scope of people's daily interactions has expanded to the point that parties from whom they are requesting services or with whom they are doing business, may not know them and thus may not be able to independently verify their identity. Thus, the problem of identity misrepresentation has developed. An extreme form of identity misrepresentation is identity theft.
The development of the Internet over the past decade has been accompanied by development of e-commerce in which two parties to a transaction are situated at distant locations and transactions are conducted via the Internet. Unfortunately, e-commerce allows for new modalities of business fraud, in particular, because the parties need not ever meet, it is possible for one party to a transaction to misrepresent their identity.
In the area of physical security, electronic systems that rely on technologies such as Radio Frequency Identification (RFID) access cards, and biometric sensors have been developed. As with any security means, these systems are not invulnerable and a sophisticated hacker may be able to undermine them.
Biometric systems take measurements such as images of a person's facial, fingerprint, retina, or iris, for example, and process the images using, for example statistical pattern recognition algorithms, in order to estimate one or more probabilities that the person being measured by the biometric system is in fact a particular person or one of a set of people whose data is stored in the biometric system.
Systems in which multiple biometric measurements are combined for the purpose of identity checking have been proposed. One way to combine multiple biometric identity probability estimates is to use the MIN function or the MAX function. However, doing so essentially discards the information represented in one of the measurements. Another way is to take two measurements that are normalized, if necessary, so that they are on the same scale and to average them. One property of averaging is that when it is applied to a high estimate that a person matches an identity, and a moderate estimate that the person matches the identity, rather than producing an even higher estimate that the person matches the identity, averaging will produce an estimate that is between the two estimates. In other words, by the process of averaging, multiple estimates that indicate, to varying degrees, that a person has a particular identity do not reinforce each other to yield an estimate that reflects a greater degree of certainty that the person matches the identity. Yet another way of combining two estimates of the probability that a person matches an identity is to multiply the two estimates.
More generally, beyond recognizing people, pattern recognition techniques can be used to recognize other things, such as spoken words, and handwritten text, for example.
What is needed is an improved system and method for combining multiple estimates of the probability that a person or thing matches an identity.
Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention.